Titlebook: Data Science in Engineering, Volume 9; Proceedings of the 4 Ramin Madarshahian,Francois Hemez Conference proceedings 2022 The Society for E

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Estimation of Structural Vibration Modal Properties Using a Spike-Based Computing Paradigm,alysis using Nengo, a large-scale neural network simulation package. In this work, we implement output-only modal identification techniques that rely on solving the blind source separation problem using spike neural networks to extract the natural frequencies, mode shapes, and damping ratios of a si

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Transmittance Anomalies for Model-Based Damage Detection with Finite Element-Generated Data and Deehe corresponding areas of the selected damaged cases. The simulated dataset is used after for training of a Deep Learning (DL) Convolutional Neural Network (CNN) classifier. The presented methodology is tested on a lab scale CFRP truss structure for which different health scenarios are considered in

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A Robust PCA-Based Framework for Long-Term Condition Monitoring of Civil Infrastructures,deemed to minimize the effect of the EOV. As such, extracting the mapped data from the original data, termed error signals, will remove the EOV effects and can be further used for damage detection. To this end, the Mahalanobis distances of the errors in the test set from the distribution of the erro

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漂泊

Classification of Rail Irregularities from Axle Box Accelerations Using Random Forests and Convolut bear tremendous potential for offering temporally and spatially dense diagnostics of railway infrastructure. While the potential of such a monitoring scheme has been proven, the generalization has been limited due to the small sample sizes in existing studies..We propose a methodology to recognize

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On a Description of Aeroplanes and Aeroplane Components Using Irreducible Element Models,ic to aeroplanes, will be developed. Additionally, aeroplanes feature various materials, geometries, and functional components that are not seen in bridges. By attempting to describe an aeroplane, the list of valid geometric, material and contextual labels within the PBSHM is expanded.

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Simulation-Based Damage Detection for Composite Structures with Machine Learning Techniques,ning framework. Simulation data is easier to generate than experimental, meaning any added value provided with simulation data is advantageous. A description of the obtained results of damage detection is presented, along with a comparative overview of the different techniques.